Quick take
Stop chasing model announcements. The teams that win in 2025 are the ones building evals, monitoring quality, and treating AI like infrastructure instead of magic. Discipline over heroics.
Every January, someone publishes a breathless AI predictions post. “This will be the year of AGI.” “Agents will replace developers.” “Multimodal everything.”
I’m not going to do that.
What I can tell you is what I see working with teams that are actually shipping AI to production. The pattern is clear: 2024 was the year everyone built demos. 2025 is the year those demos have to work.
The demo hangover
Here’s what happened to most AI projects last year. Someone built a prototype in a weekend. It was impressive. Leadership got excited. Budget appeared. Then the prototype hit real users, real data, and real edge cases, and everything got complicated.
I watched this play out at three different companies. Same story every time. The model was fine. The engineering around the model wasn’t.
Missing evaluation suites. No fallback paths. Prompts that drifted every time someone tweaked them. Cost tracking that amounted to “we’ll figure it out later.” The model was the easy part. Operating discipline was the hard part.
That’s the real trend for 2025. Not a new model. A new level of engineering rigor around models.
Reasoning gets interesting
Models that think before they answer are genuinely useful for a specific class of problems. Multi-step analysis. Code review. Debugging. Anything where you would rather wait 30 seconds for a correct answer than get a fast wrong one.
The trap is treating reasoning models as the default. They’re slower, more expensive, and overkill for 80% of requests. The smart move is routing: fast model for simple tasks, reasoning model for complex ones. I’ll write more about this in a couple of weeks.
Multimodal is real but boring
Image, audio, and text working together is no longer a research demo. It’s a feature. Internal tools are the clearest win – think document-processing pipelines that can read scanned forms, or support systems that understand screenshots.
The value isn’t in any single modality being amazing. It’s in combining them so the system has richer context. Boring. Useful. Exactly the kind of thing that makes money.
Evaluation-first development
The single biggest shift I keep pushing is simple: define success before you write the first prompt.
This sounds obvious. Almost nobody does it. Teams will spend weeks tuning prompts and then measure success by vibes. “It feels better.” “The CEO liked the demo.” That isn’t engineering. That’s hope.
What works: a fixed eval set, tested on every change, with clear pass/fail criteria. Treat prompts like code. Version them. Review them. Test them. I won’t ship a prompt change without running it against the eval suite. Period.
Governance stops being optional
Regulation is firming up. The EU AI Act is real. Enterprise clients are asking for audit trails, documentation, and risk tiers before they’ll sign contracts. If your AI system can’t explain what it does, what data it touches, and who’s responsible when it goes wrong, you’re in for a bad year.
This isn’t bureaucracy for its own sake. Good governance actually accelerates adoption because it turns “can we use AI for this?” from a six-week debate into a checklist. Risk tier low? Ship it. Risk tier high? Here’s exactly what you need before you ship.
Governance that blocks delivery is broken governance. Governance that makes yes safe and fast is a competitive advantage.
Agents: promising, overhyped
Agents that can execute multi-step tasks are improving fast. They’re also still brittle. Context changes break them. Domain boundaries confuse them. The failure modes are subtle and hard to detect.
The near-term play is constrained agents with explicit checkpoints. Not open-ended autonomy. Not “let the agent figure it out.” Clear scope, clear permissions, clear rollback. We learned this lesson with microservices a decade ago: autonomy without contracts is chaos.
What I’m ignoring
- Any roadmap built on vendor keynote slides instead of product outcomes.
- Prompt engineering tricks that can’t be tested, versioned, or reproduced.
- “Autonomous” systems with no permission model, no audit trail, and no kill switch.
- Anyone who says “just add AI” without specifying what success looks like.
What matters
The capabilities are real. The models will keep getting better. But the gap between “this works in a demo” and “this works in production at 3am on a Saturday” is where careers and companies are made.
Ruthless focus on the boring stuff. Evals. Monitoring. Cost tracking. Fallback paths. Governance. That’s the 2025 playbook.
The teams that treat AI like infrastructure – with the same rigor they bring to databases and deployment pipelines – will win. Everyone else will keep rebuilding demos.